I will be on the 2020-21 finance job market. Download my job market materials here.
My research interests include studying financial markets in the large and in the small.
I maintain a public Call For Papers calendar that might interest you.
My wife and I enjoy spending our free time entertaining our son and getting outside when we can. Things I want to brag about but decided against putting on my CV: (1) completed multiple marathons, (2) won a few amateur ballroom dancing awards (credit due to my beautiful dancing partner), and (3) good listener.
Market clearing requires that aggregate consumption equals the sum of capital and labor income. Yet, dividend processes in leading asset pricing models ignore this constraint. I propose to model the labor share of consumption and obtain dividends from market clearing, rather than modeling dividends directly. The approach is parsimonious, tractable, delivers cointegration between consumption and dividends, and captures the effect of labor market frictions on equity payout. When embedded into the habit and long-run risks models, the cash flow process allows the models to capture otherwise puzzling facts about the term structures of cash flow risk and equity risk premia.
We use options and return data to decompose unconditional risk premia into different parts of the return state space. In the data, the entire equity premium is attributable to monthly returns below -11.3%, but returns in the extreme left tail matter very little. In contrast, leading asset pricing models based on habits, long-run risks, and rare disasters attribute the premium almost exclusively to returns above -11.3%, or to the extreme left tail. We find that model extensions with a larger quantity of tail risk cannot account for the data, while models with a higher price of tail risk can.
We study the anatomy of four widely used institutional trading algorithms representing $675 billion in demand from 961 institutions between 2012 and 2016. Parent orders generate hundreds of child orders which strategically employ price, time-in-force, and display priority rules to navigate the tradeoff between the desire to trade and minimizing transaction costs. Child orders incur price impact at the time they are submitted to the book regardless of whether or not they are (ex post) filled, and even when passively priced relative to the prevailing quote. The intra-parent distribution of child orders is non-random, generating strategic runs which oscillate between the aggressive or passive side of the spread. Despite algorithmic attempts to reduce their influence, programmatic child-level price, time-in-force, and display choices aggregate up to parent-level trading costs borne by investors.
I study the distributional properties of household risky shares, the fraction of their financial portfolio allocated to risky assets. Many proposed solutions to bring household life-cycle portfolio choice models in line with the average risky share, such as participation costs or differences in labor income risk profiles, fall far short of generating sufficient cross-sectional heterogeneity in portfolio allocations at nearly every point in the life-cycle.
I provide a new perspective on the process of repeated saving using financial products with potentially uncertain rates of return, such as savings, brokerage, or retirement accounts. A financial account can be viewed as a portfolio of investments in itself with differing horizon – it follows that standard portfolio theory applies. The analysis makes extensive use of moment generating functions, and I pursue two approaches for tractability. I first develop a novel iterative algorithm for computing multivariate moment generating functions, while the second approach exploits a truncated moment generating function approximation. I discuss the benefits of this framework for financial planners and policymakers, how it relates to current modeling techniques, and possible extensions to multiple assets and uncertain savings amounts.
We use kinetic Monte Carlo simulations to produce solutions of an agent-based, rate equation model of an informationally efficient, closed financial market. The simulations produce a crash in the market that is forewarned through the observation of a market instability from which the market temporarily recovers. The market remained in a quasi-stable state for a relatively large amount of time between the warning and the crash, raising the prospect that some mitigating action can be taken in time to avert the impending crash. This result has strong ramifications for the future of predicting calamitous market events, especially if some observable aspect of financial markets can be positively identified and associated with simulation parameters.
I am a proponent of open source software and transparent academic research. I am an active member of the Julia community.
In addition to contributing to existing Julia packages, I maintain a number of packages I found useful in my own work. I hope that other finance researchers and practitioners can find value in them as well.
FamaFrenchData.jl – cleanly pull data directly from the Ken French Data Library
DailyTreasuryYieldCurve.jl – efficiently pull yield curves directly from the US Treasury
FinancialPortfolios.jl – convenience package for working with portfolios
AsymmetricRisk.jl – implements a number of univariate and bivariate asymmetric risk measures (WIP)
This website is also open source.